Evidence-based approaches to childhood stunting in low and middle income countries: a systematic review
Bibliographic record
Abstract
OBJECTIVE: We systematically evaluated health and nutrition programmes to identify context-specific interventional packages that might help to prioritise the implementation of programmes for reducing stunting in low and middle income countries (LMICs). METHODS: Electronic databases were used to systematically review the literature published between 1980 and 2015. Additional articles were identified from the reference lists and grey literature. Programmes were identified in which nutrition-specific and nutrition-sensitive interventions had been implemented for children under 5 years of age in LMICs. The primary outcome was a change in stunting prevalence, estimated as the average annual rate of reduction (AARR). A realist approach was applied to identify mechanisms underpinning programme success in particular contexts and settings. FINDINGS: Fourteen programmes, which demonstrated reductions in stunting, were identified from 19 LMICs. The AARR varied from 0.6 to 8.4. The interventions most commonly implemented were nutrition education and counselling, growth monitoring and promotion, immunisation, water, sanitation and hygiene, and social safety nets. A programme was considered to have effectively reduced stunting when AARR≥3%. Successful interventions were characterised by a combination of political commitment, multi-sectoral collaboration, community engagement, community-based service delivery platform, and wider programme coverage and compliance. Even for similar interventions the outcome could be compromised if the context differed. INTERPRETATION: For all settings, a combination of interventions was associated with success when they included health and nutrition outcomes and social safety nets. An effective programme for stunting reduction embraced country-level commitment together with community engagement and programme context, reflecting the complex nature of exposures of relevance. PROSPERO REGISTRATION NUMBER: CRD42016043772.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.081 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.009 |
| Bibliometrics | 0.025 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".